The Verification of Hazardous Ingredients Disclosures in Selected Material Safety Data Sheets
Bibliographic record
Abstract
Under the provisions of the Workplace Hazardous Materials Information System, workers in Canada must be provided with accurate and comprehensive Material Safety Data Sheets (MSDSs) describing controlled products used in the workplace. As part of an ongoing auditing project, the MSDSs of some controlled products in use under federal jurisdiction were assessed for accuracy and completeness of their ingredient disclosures. Chemical analyses of samples using gas chromatography-mass spectrometry, infrared spectrophotometry, X-ray fluorescence, and wet methods, were performed to verify the ingredient disclosures in accompanying MSDSs. In this article, analytical processes and results are presented for three cases in which MSDS ingredient disclosures were incomplete. The products included a synthetic lubricant used in a mining operation, a detergent concentrate used for aircraft cleaning, and an epoxy reducer used in aircraft maintenance. In each case, undisclosed hazardous ingredients were detected at concentrations which required their disclosure. In at least one of these cases, the information provided in other sections of the MSDS failed to adequately describe the hazards and required protective measures for the composition discovered. Because the results suggest circumstances in which the inaccurate MSDS could act as a mechanism for workplace injury, compliance measures including employer, inspector, and user education, improved MSDS writer qualifications, and the incorporation of chemical analysis in active auditing programs are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".